Observe in context
Bring telemetry, asset relationships, manuals, and service history into the finding.
TwinEdge Agentic Analytics
A signal. An explanation. A next step.
Predictive analytics and AI-native agents for industrial work.
TwinEdge Agentic Analytics is AI-native. Agents read live equipment history, explain extra cost or wear, and draft the next job. A person approves.
AI-native agentic analytics AI-NATIVE AGENTS
Specialist agents share one operating record — diagnostics, planner, inventory, capital, and knowledge.
START WITHCondition + operating history
TWINEDGE CONNECTSA finding becomes a next step
MOVE FORWARD WITHAction + result
THE WORKFLOW
From signal context to approved recommendation
Build the response
Diagnose the issue and prepare maintenance, inventory, or operating recommendations.
Follow the action
Keep the decision, work, and resulting evidence connected for the next analysis.
Explain the reasoning
Evidence travels with the recommendation.
Coordinate specialists
Diagnostics, maintenance, inventory, and capital agents share context.
Move beyond the alert
The finding reaches the workflow that can resolve it.
FROM THE PRODUCT
See the work in context.
Select a screen to explore the workflow. Open it for a closer look.
01 / INSIDE TWINEDGE
Coordinate the agent team
The agent platform brings specialist roles, findings, and pending decisions into a shared operating view.
BUILT FOR THE OPERATION
Predictive analytics and agent capabilities
Predictive analytics
Estimate likely wear, extra energy, and failure risk from live condition, operating history, and physics context , then attach the evidence to the asset.
Recommendation workspace
Evidence-backed findings with source links, asset context, physics signals, risk, and suggested actions.
Approval gates
Dry-run plans, diffs, eval gates, and human review before routing work or publishing products.
AI data products
REST and MCP surfaces expose governed operational context to trusted apps and AI clients.
CONNECTED BY DESIGN
Fits the systems around it.
Keep source context available as the workflow moves between teams and applications.
One connected record.
TwinEdge Agentic Analytics
- DataOps Workbench creates the physics-aware, AI-ready context.
- Agentic Analytics uses that context to explain, draft, and validate recommendations.
- TwinEdge OS supports cloud-connected, offline, and protocol-rich edge deployments.
- AssetOps EAM and TwinEdge Field close the loop from recommendation to work, guided mobile diagnostics, human-reviewed evidence, and closeout.
- Water OS, Wastewater OS, TwinEdge Chemical, Water Loss OS, Water Quality, Capital Planning, ESG, and Facility OS package industry workflows.
- REST and MCP data products make context available to enterprise applications and AI systems.
BEFORE YOU START
A few useful answers.
What is TwinEdge predictive analytics?
TwinEdge predictive analytics estimates likely future equipment behavior from live condition, operating history, physics models, and work records. Agentic Analytics turns that estimate into an explainable finding and a drafted next step for a person to approve.
What is an industrial AI agent in TwinEdge?
A TwinEdge industrial AI agent works inside a bounded operational task. It reads approved asset and process context, explains evidence, drafts a recommendation or work package, validates policy, and records the result for replay.
How is Agentic Analytics different from a general AI chatbot?
General chat starts from a prompt. TwinEdge starts from governed industrial context, physics outputs, source lineage, tools, roles, approval rules, and a defined operational handoff.
Do agents autonomously control equipment?
No. TwinEdge is recommendation-first and read-only by default. Agents can draft and validate proposed actions, but a human approval gate controls operational handoff unless a separately governed policy explicitly permits more.
What evidence is kept with an AI recommendation?
Recommendations can retain source records, asset context, physics and quality signals, assumptions, dry-run output, approval history, work handoff, and final closeout evidence.
Can external AI clients use TwinEdge context?
Approved applications and agents can use tenant-scoped REST APIs and MCP tools that expose governed operational context without granting unrestricted access to source systems.
THE NEXT CONNECTION
Explore the product familyBuild the rest of your loop.
START WITH YOUR OPERATION
Put the next decision in motion.
Discuss your sources, assets, and the outcomes your team wants to improve.